{"id":"W3089923376","doi":"10.1109/icra40945.2020.9197217","title":"AC/DCC : Accurate Calibration of Dynamic Camera Clusters for Visual SLAM","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Calibration; Gimbal; Computer science; Artificial intelligence; Computer vision; Noise (video); Camera auto-calibration; Projection (relational algebra); Collinearity; Fiducial marker; Camera resectioning; Joint (building); Algorithm; Mathematics; Engineering; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001062856,0.001363311,0.0009008667,0.001370671,0.0009157312,0.001150838,0.002014066,0.001005819,0.004917158],"category_scores_gemma":[0.0047881,0.0007741931,0.0005068697,0.002147176,0.0008394931,0.001463928,0.00356332,0.002251167,0.002935308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118581,"about_ca_system_score_gemma":0.002081915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004011,"about_ca_topic_score_gemma":0.01370058,"domain_scores_codex":[0.9981002,0.0002304629,0.00003936357,0.0004841389,0.000957738,0.000188105],"domain_scores_gemma":[0.9984268,0.0001591897,0.0001531085,0.0006639436,0.0004855974,0.0001114178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004127989,0.0001720262,0.00336827,0.0002460477,0.0001335179,0.0001556359,0.0003310893,0.3536008,0.05129508,0.01043332,0.02517619,0.5546753],"study_design_scores_gemma":[0.00004892151,0.00007620912,0.001780758,0.00002959044,0.00001148728,0.0001741078,0.0000870088,0.9601158,0.02094074,0.005265786,0.01141266,0.00005680593],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01583201,0.0001600226,0.9731693,0.0001180726,0.00009235684,0.00007521689,0.0003089962,0.00686278,0.003381222],"genre_scores_gemma":[0.3663099,0.0001318262,0.6273226,0.0001501753,0.00006495912,0.0002048298,0.00142976,0.001340016,0.003045926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01004011,"threshold_uncertainty_score":0.01996332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351805737665811,"score_gpt":0.2417081385376863,"score_spread":0.2281900811610282,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}